A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning
Modelling aging in the second life of lithium-ion batteries (LiBs) is challenging due to the complexity of degradation mechanisms that lead to capacity loss and internal resistance increase, as well as uncertainty and variability in the operational and environmental conditions to which the batteries...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7378 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849429054505091072 |
|---|---|
| author | Daniela Galatro Cristina H. Amon |
| author_facet | Daniela Galatro Cristina H. Amon |
| author_sort | Daniela Galatro |
| collection | DOAJ |
| description | Modelling aging in the second life of lithium-ion batteries (LiBs) is challenging due to the complexity of degradation mechanisms that lead to capacity loss and internal resistance increase, as well as uncertainty and variability in the operational and environmental conditions to which the batteries are exposed. In this work, we propose a similarity-based approach for diagnosing the aging of LiBs in their second life, which combines time series analysis and machine learning to help identify trends and patterns in the aging process. This approach overcomes the intrinsic nonlinearity nature of the LiB aging trajectory in the second life while adapting to varying operational and environmental conditions. Knees or inflection points defining the first, second, and non-usable lives of the batteries are also identified, offering insights into degradation mechanisms and thus supporting thermal management and optimal user-pattern tasks to extend the LiBs’ lifetime. |
| format | Article |
| id | doaj-art-15f757fb488a49ae9e702ed7ba94dfd4 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-15f757fb488a49ae9e702ed7ba94dfd42025-08-20T03:28:29ZengMDPI AGApplied Sciences2076-34172025-06-011513737810.3390/app15137378A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine LearningDaniela Galatro0Cristina H. Amon1Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, CanadaDepartment of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, CanadaModelling aging in the second life of lithium-ion batteries (LiBs) is challenging due to the complexity of degradation mechanisms that lead to capacity loss and internal resistance increase, as well as uncertainty and variability in the operational and environmental conditions to which the batteries are exposed. In this work, we propose a similarity-based approach for diagnosing the aging of LiBs in their second life, which combines time series analysis and machine learning to help identify trends and patterns in the aging process. This approach overcomes the intrinsic nonlinearity nature of the LiB aging trajectory in the second life while adapting to varying operational and environmental conditions. Knees or inflection points defining the first, second, and non-usable lives of the batteries are also identified, offering insights into degradation mechanisms and thus supporting thermal management and optimal user-pattern tasks to extend the LiBs’ lifetime.https://www.mdpi.com/2076-3417/15/13/7378aginglithium-ion batteriessecond lifetime seriesmachine learningknees |
| spellingShingle | Daniela Galatro Cristina H. Amon A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning Applied Sciences aging lithium-ion batteries second life time series machine learning knees |
| title | A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning |
| title_full | A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning |
| title_fullStr | A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning |
| title_full_unstemmed | A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning |
| title_short | A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning |
| title_sort | similarity based approach for diagnosing the aging of lithium ion batteries in second life combining time series and machine learning |
| topic | aging lithium-ion batteries second life time series machine learning knees |
| url | https://www.mdpi.com/2076-3417/15/13/7378 |
| work_keys_str_mv | AT danielagalatro asimilaritybasedapproachfordiagnosingtheagingoflithiumionbatteriesinsecondlifecombiningtimeseriesandmachinelearning AT cristinahamon asimilaritybasedapproachfordiagnosingtheagingoflithiumionbatteriesinsecondlifecombiningtimeseriesandmachinelearning AT danielagalatro similaritybasedapproachfordiagnosingtheagingoflithiumionbatteriesinsecondlifecombiningtimeseriesandmachinelearning AT cristinahamon similaritybasedapproachfordiagnosingtheagingoflithiumionbatteriesinsecondlifecombiningtimeseriesandmachinelearning |